41–50 of 70 posts
Can this be applied to camera shutter/motion blur, at low speeds the slight shake of the camera produces this type of blur. This is usually resolved with IBIS to stabilize the sensor.
Its somewhere here: https://www.microsoft.com/en-us/research/product/computation...
Blur is perhaps surprisingly one of the degradations we know best how to undo. It's been studied extensively because there's just so many applications, for microscopes, telescopes, digital cameras. The usual tricks revolve around inverting blur kernels, and making educated guesses about what the blur kernel and underlying image might look like. My advisors and I were even able to train deep neural networks using only…
Can this be applied to camera shutter/motion blur, at low speeds the slight shake of the camera produces this type of blur. This is usually resolved with IBIS to stabilize the sensor.
I believe Microsoft of all people solved this a while ago by using the gyroscope in a phone to produce a de-blur kernel that cleaned up the image. Its somewhere here: https://www.microsoft.com/en-us/research/product/computation...
Sorry but this post is the blind leading the blind, pun intended. Allow me to explain, I have a DSP degree. The reason the filters used in the post are easily reversible is because none of them are binomial (i.e. the discrete equivalent of a gaussian blur). A binomial blur uses the coefficients of a row of Pascal's triangle, and thus is what you get when you repeatedly average each pixel with its neighbor (in 1D). Wh…
Earlier quoted context omitted.
> just mask the sensitive data with a single color which is impossible to reverse (for rasterized images, this is not a good idea for PDFs Also not a good idea for masking already compressed images of text, like jpg, because some of the information might bleed out in uncovered areas.
Interesting - does a little extra coverage solve this or is it possible to use distant pixels to find the original?
JPEG compression can only move information at most 16px away, because it works on 8x8 pixel blocks, on a 2x down-sampled version of the chroma channels of the image (at least the most common form of it does)
Earlier quoted context omitted.
> just mask the sensitive data with a single color which is impossible to reverse (for rasterized images, this is not a good idea for PDFs Also not a good idea for masking already compressed images of text, like jpg, because some of the information might bleed out in uncovered areas.
Interesting - does a little extra coverage solve this or is it possible to use distant pixels to find the original?
Except the size of the blocked section ofc. E.g If you know it's a person's name, from a fixed list of people, well "Huckleberry" and "Tom" are very different lengths.
Captain Disillusion recently covered this subject in a more popular science format as well https://youtu.be/xDLxFGXuPEc
8 months ago, for those of us who got excited by the idea of a "recent" new video from CD.
Blur is perhaps surprisingly one of the degradations we know best how to undo. It's been studied extensively because there's just so many applications, for microscopes, telescopes, digital cameras. The usual tricks revolve around inverting blur kernels, and making educated guesses about what the blur kernel and underlying image might look like. My advisors and I were even able to train deep neural networks using only…
Can this be applied to camera shutter/motion blur, at low speeds the slight shake of the camera produces this type of blur. This is usually resolved with IBIS to stabilize the sensor.
Absolutely, Photoshop has it: https://helpx.adobe.com/photoshop/using/reduce-camera-shake-... Or... from the note at the top, had it? Very strange, features are almost never removed . I really wonder what the architectural reason was here.
Sorry but this post is the blind leading the blind, pun intended. Allow me to explain, I have a DSP degree. The reason the filters used in the post are easily reversible is because none of them are binomial (i.e. the discrete equivalent of a gaussian blur). A binomial blur uses the coefficients of a row of Pascal's triangle, and thus is what you get when you repeatedly average each pixel with its neighbor (in 1D). Wh…
Emphasis mine. Quote from the beginning of the article.
This isn't meant to be a textbook about blurring algorithms. It was supposed to be a demonstration of how what may seem destroyed to a causal viewer is recoverable by a simple process, intended to give the viewer some intuition that maybe blurring isn't such a good information destroyer after all.
Your post kind of comes off like criticizing someone for showing how easy it is to crack a Caesar cipher for not using AES-256. But the whole point was to be accessible, and to introduce the idea that just because it looks unreadable doesn't mean it's not very easy to recover. No, it's not a mistake to be using the Caesar cipher for the initial introduction. Or a dead-simple one-dimensional blurring algorithm.